GODFATHER

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GODFATHER

GODFATHER

@T__GODFATHER

AMBASSADOR WEB3 RESEARCHER ARTIST CONTENT CREATOR NODE RUNNER

Katılım Şubat 2023
1.1K Takip Edilen1K Takipçiler
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GODFATHER
GODFATHER@T__GODFATHER·
🔥Clasho Gain early access to earn $25,000 Get paid to make content for the biggest brands in the world 🔥only have 14 hours left for early access. @clashoAi clasho.com/invite/TPPP44TH
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GODFATHER
GODFATHER@T__GODFATHER·
🔥Clasho Gain early access to earn $25,000 Get paid to make content for the biggest brands in the world 🔥only have 14 hours left for early access. @clashoAi clasho.com/invite/TPPP44TH
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Alfie
Alfie@Sae3ds·
What you’ve been waiting for is finally here You can now check your Season 1 SLX allocation on the @solsticefi dashboard, Just head over, connect your wallet, and you’ll see your tokens in the $SLX section app.solstice.finance/earn-flares At FDV $100M : $5,000 At FDV $200M: $10,000 Thank you Solstice ❤️ if you’re not part of #Solstice yet, you can join now through the link below and use my code to receive 1,000 Flares app.solstice.finance/earn-flares Code: bDY5aeKKDm
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Clasho
Clasho@clashoAi·
$25,000. 6 of the biggest companies in the world. The best content wins. Attention is the new currency. Reveal → May 6. 2026. Limited access. 👇🏻 clasho.com
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ZOHRE
ZOHRE@zohrehfakhri·
First the problem Fermah is targeting is absolutely legitimate. In the broader context of Zero Knowledge Proofs, generating proofs especially for systems like zk-rollups is computationally expensive hardware sensitive and often centralized in practice. Even ecosystems built on Ethereum struggle with this because proof generation can become a bottleneck despite improvements in verification efficiency. And fermah targets these problems @fermah_xyz @flashcaster #ZK #Web3 #Crypto #Fermah @0xTribal @vanishree_rao @7wealthh
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ZOHRE@zohrehfakhri

Fermah which is currently positioning itself as the universal proof generation layer for the zero knowledge (ZK) ecosystem. By framing proof generation as a two sided marketplace Fermah addresses one of the biggest bottlenecks in blockchain scaling: the massive specialized compute power required to generate ZK proofs. @fermah_xyz @flashcaster #ZK #Web3 #Crypto #Fermah @0xTribal @vanishree_rao @7wealthh

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GODFATHER
GODFATHER@T__GODFATHER·
Mint Successful ✅ Name: 110🔥 Nft from @AriseVerse @Arise_Market Launchpad. Chain: @0G_labs FCFS mint begins at 4 PM UTC. Public mint will be later Be ready
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Degen 🎩
Degen 🎩@degentokenbase·
A creature unlike any other. A dreamer, a disruptor … a … DEGEN. 777 supply. Free mint. Q1 2026 Enter the dream → dream.degen.tips
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GODFATHER
GODFATHER@T__GODFATHER·
@nabi_sarvi @myfanforce Every project that tries to ignore its community is destined to fail learn from the ones that made this mistake before.
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GODFATHER
GODFATHER@T__GODFATHER·
@dweor_ How does GenLayer ensure that subjective AI driven validation remains trustworthy and resistant to bias?
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Dweor
Dweor@dweor_·
Rally works only because judgment can be decentralized, not reduced to rigid rules. When a campaign asks Is this content actually good??? that is a subjective question. Counting likes or followers cannot answer it. Rally depends on GenLayer because GenLayer allows non deterministic, AI native validation where multiple independent validators reason over context, originality, and intent, then converge on a decision. That is infrastructure, not hype. GenLayer matters because real world coordination is messy. Creators produce different styles, audiences react in different ways, and quality cannot be measured with a single metric. GenLayer’s validator model lets Rally evaluate nuance instead of enforcing brittle logic. This makes it possible to run campaigns that reward creativity rather than optimization tricks. Without this layer, Rally would collapse into another engagement farm. With it, subjective evaluation becames a shared, trustless process. That is why @RallyOnChain is not just deployed on GenLayer, it is structurally dependent on what @GenLayer enables.l
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GODFATHER
GODFATHER@T__GODFATHER·
@nabi_sarvi @KovaNetwork @KovaNetwork : Top DePIN project for decentralized liquid compute.Per-second + fractional GPU billing Smart checkpointing Global GPUs, no lock-in 50-90% cheaper AI/inference/rendering 80-90% utilization Strong team, game-changer for devs & startups
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NabiYok 🇮🇷
NabiYok 🇮🇷@nabi_sarvi·
Since my expertise is in CGI and graphic design, I wanted to wrap up the @KovaNetwork article writing campaign with this logo motion😉 Reviewing KOVA and understanding the weaknesses of traditional compute clouds is something that builders and small developers, especially in AI really desperately need. However, due to X new rules, I have to point out that my content is a theoretical examination of KOVA's excellent structure and not financial advice Big thanks to @myfanforce for giving me the opportunity to get to know KOVA and putting me on this path ❤️
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GODFATHER
GODFATHER@T__GODFATHER·
@nabi_sarvi The Uber comparison nailed it Kova is basically turning compute into an on demand ride: you only pay for the seconds you actually use, instead of renting the whole idle GPU that sits 70% wasted.
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GODFATHER
GODFATHER@T__GODFATHER·
@nabi_sarvi درود نبی جان لینک چنل تلگرام رو بذار
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GODFATHER
GODFATHER@T__GODFATHER·
@zahrazareiw In Kova how much extra latency do the verifiable proofs actually add? Is it still fine for real time inference or is it mostly suitable for batch jobs?
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GODFATHER
GODFATHER@T__GODFATHER·
@yaasaamaan1 @KovaNetwork How does this model ensure that GPU usage is measured and billed accurately when workloads run only on demand?
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yasaman 🐜
yasaman 🐜@yaasaamaan1·
Cloud doesn’t really sell computing it sells reserved capacity. You pay for machines to sit “just in case.” @KovaNetwork flips that: compute becomes consumption. Workloads run across decentralized providers, execution is defined with SDL, billing streams via on-chain escrow, and GPUs are accessed only when active. Capacity economics → usage economics Powered by @myfanforce
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GODFATHER
GODFATHER@T__GODFATHER·
@a3mir78 In @KovaNetwork proof of compute system how exactly do they score or penalize a failed task? Is it just a flat penalty or does the weight change depending on whether the job was inference vs training?
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